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As noted above, there are various issues with the analysis as completed so far.
Below I discuss various issues and ways to address them that I believe 
will improve the analysis.

\subsection{Increasing number of observations}

The most important step is to increase the number of observations available,
specifically the number of trials matched to ICD-10 codes with corresponding
population estimates in the Global Burden of Disease Dataset.
Improvements in Large Language Models may make this data more accessible, or
the data may be available in a commercial dataset.


\subsection{Enrollment Modelling}

One of the original goals of this project was to examine the impact that 
enrollment struggles have on the probability of trial termination. 
Unfortunately, this requires a model of clinical trial enrollment, and this
data is missing from my dataset.
In most cases the trial sponsor reports the anticipated enrollment value
while the trial is still recruiting and only updates the actual enrollment
after the trial has ended.
Some trials do publish an incremental record of their enrollment numbers, 
but this is not the norm. 
It may be possible to impute the enrollment process if a suitible model
can be created.
% Due to the bayesian model used, this would be easy to incorporate
% \cite{mcelreath_statisticalrethinkingbayesian_2020}.

There has been substantial work on forecasting
multi-site enrollment rates and durations by
\cite{
    tozzi_predictingaccrualrate_1996,
    carter_applicationstochasticprocesses_2004,
    anisimov_modellingpredictionadaptive_2007,
    zhang_stochasticmodelingprediction_2010,
    zhang_jointmonitoringprediction_2012,
    zhang_modelingpredictionsubject_2012,
    heitjan_realtimepredictionclinical_2015,
    jiang_modelingvalidatingbayesian_2015,
    deng_bayesianmodelingprediction_2017,
    lan_statisticalmodelingprediction_2019,
    zhang_simplerobustmodel_2022,
    urbas_interimrecruitmentprediction_2022,
    bieganek_predictionclinicaltrial_2022,
    avalos-pacheco_validationpredictiveanalyses_2023,
    }
but choosing between the various single and multi-site models presented is 
difficult without a dataset with which to validate the results.
% In addition to needing a well calibrated model, I would require more trials,
% specifically those that report their enrollment incrementally so 
% that there is data on what happens when enrollment is slower than anticipated.
% It may also be possible to estimate the probability that enrollment goals
% have been met if data can be extracted that details planned observation times.
% Of course, this is speculative at this point.
%FIXTAG: Avoid speculation here.

% \subsection{Improving Population Estimates}
%
% The Global Burden of Disease dataset contains the best estimates of disease
% population sizes that I have found so far. 
% Unfortunately, for some conditions it can be relatively imprecise due to 
% its focus on providing data geared towards public health policy.
% For example, GBD contains categories for both
% drug resistant and drug suceptible tuberculosis, but maps those to the same 
% ICD-10 code.
% In contrast, there is no category for non-age related macular degeneration.
% Thus not every trial has a good match with the estimate of the population of
% interest.
% Finding a way to focus on trials that have good disease population estimates
% would improve the efficiency of the analysis.
% %FIXTAG: What am I trying to say here. IHME is among the best data sources.
% % How do I propose getting other data? Should probably just remove this.

\subsection{Improving Measures of Market Conditions}

% Deficiency: cannot measure effect of market conditions because of endogenetiy of population and market conditions (fatal diseases)

In addition to the fact that many diseases may be treated by non-pharmaceutical 
means (e.g. diet, physical therapy, medical devices, etc), 
off-label prescription of pharmaceuticals is legal at the federal level 
\cite{commissioner_understandingunapproveduse_2019}.
%FIXTAG: Discuss how there isn't much data about off label prescription (I have a source)
These two facts both complicate measuring competing treatments, 
a key part of market conditions.
One way to address non-pharmaceutical treatments is to concentrate on domains
that are primarily treated by pharmaceuticals.
Another way to address this would be to focus the analysis on just a few specific
diseases, for which a history of treatment options can be compiled.
%FIXTAG: Get rid of 'another', doesn't match context
This second approach may also allow the researcher to distinguish the direction
of causality between population size and number of drugs on the market; 
%FIXTAG: join better to prior sentence
for example, drugs to treat a chronic, non-fatal disease will probably not 
affect the market size much in the short to medium term.
This would require identifying diseases that are prime candidates and then
trials and drugs associated with those diseases.
% This allows the effect of market conditions to be isolated from
% the effects of the population.
% %FIXTAG: I am already proposing these as fixes

% Alternative approaches 
% - diseases with constant kill rates? population effect should be relatively constant?

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